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db/db_default__vector_store.json

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"_node_type": "TextNode", "document_id": "ac226b84-1585-4759-add3-dc5d0af6ef65", "doc_id": "ac226b84-1585-4759-add3-dc5d0af6ef65", "ref_doc_id": "ac226b84-1585-4759-add3-dc5d0af6ef65"}}}

db/db_docstore.json

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+{"docstore/metadata": {"ac226b84-1585-4759-add3-dc5d0af6ef65": {"doc_hash": "2c629fa1f2e1e85f17b7d012739aea7cba30cdd55f935ed2225710942132eabf"}, "860863cb-ba90-4287-9ff3-de812a7cf04a": {"doc_hash": "2c629fa1f2e1e85f17b7d012739aea7cba30cdd55f935ed2225710942132eabf", "ref_doc_id": "ac226b84-1585-4759-add3-dc5d0af6ef65"}}, "docstore/data": {"860863cb-ba90-4287-9ff3-de812a7cf04a": {"__data__": {"id_": "860863cb-ba90-4287-9ff3-de812a7cf04a", "embedding": null, "metadata": {"page_label": "1", "file_name": "saved_pdf.pdf", "file_path": "E:\\llama-index RAG\\data\\saved_pdf.pdf", "file_type": "application/pdf", "file_size": 64903, "creation_date": "2024-04-14", "last_modified_date": "2024-04-17"}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {"1": {"node_id": "ac226b84-1585-4759-add3-dc5d0af6ef65", "node_type": "4", "metadata": {"page_label": "1", "file_name": "saved_pdf.pdf", "file_path": "E:\\llama-index RAG\\data\\saved_pdf.pdf", "file_type": "application/pdf", "file_size": 64903, "creation_date": "2024-04-14", "last_modified_date": "2024-04-17"}, "hash": "2c629fa1f2e1e85f17b7d012739aea7cba30cdd55f935ed2225710942132eabf", "class_name": "RelatedNodeInfo"}}, "text": "3.DataProblem\nThisdocumentoutlinesthespecificinstructionsforpreparingtheprovideddatabaseofhumanvoice\nrecordingsfortrainingamachinelearningmodelcapableofdistinguishingbetweenauthenticand\nsyntheticvoices.\n1.DataExplorationandAnalysis:\n\uf0fc UtilizetoolssuchasMatplotlibandSeabornforin-depthdataanalysisandvisualization.\n\uf0fc Beginwithacomprehensiveexplorationofthedatabase,understandingcharacteristics,and\nassessingthedistributionofauthenticandsyntheticsamples.\n\uf0fc Identifyandaddressimbalancedsamplesinthedataset.\n2.ImbalanceHandling:\n\uf0fc Enhancemodelperformancebyemployingtechniquessuchasoversamplingorundersampling,\ne.g.,usingSMOTEorImblearn.\n3.DataCleaning:\n\uf0fc Addressvariationsinsamplewavlengthbyfindingthemeanoftotalsamplelengths.\n\uf0fc Utilizepaddingtechniquestostandardizeeachsampletothefixedmeanlength.\n\uf0fc Handlemisclassifiedsampleswithinthedataset.\n4.FeatureEngineering:\n\uf0fc ExtractrelevantacousticfeatureslikeMFCCs,spectrograms,andpitchfromaudiorecordings.\n\uf0fc Experimentwithdifferentfeaturesetstoidentifythemostdiscriminativeones.\n\uf0fc Normalizeandstandardizefeaturesforconsistentscaling,facilitatingmodeltraining.\n5.SpeakerEmbeddings:\n\uf0fc Considerincorporatingspeakerembeddingstocaptureindividualcharacteristics,enhancingthe\nmodel'sabilitytogeneralizeacrossdiversevoices.\n\uf0fc Implementsuitablemethodsforextractingspeakerembeddings,suchaspre-trainedmodelsor\ntrainingonthedataset.\n6.DataSplitting:\n\uf0fc Splitthedataintotraining,validation,andtestsets,ensuringastratifiedsplit.\n\uf0fc Evaluatemodelperformanceonthevalidationset,minimizinglossbeforefinaltestingonthe\ntestsamples.\n7.DataAugmentation:\n\uf0fc Applydataaugmentationtechniquestoincreasemodelrobustnessagainstvariationsin\nrecordingconditions.\n\uf0fc Techniquesmayincluderandompitchshifts,time-stretching,orintroducingbackgroundnoise.\n8.QualityControl:\n\uf0fc Conductarigorousqualitycontrolchecktoidentifyandaddressanomaliesoroutliersinthe\ndataset.\n\uf0fc Verifythatdatapreprocessingstepsdonotintroduceartifactsnegativelyaffectingmodel\nperformance.\nOncethedataispreparedfollowingtheseguidelines,thetransitionintothemodeldevelopment\nphasewillfocusonselectinganappropriatearchitecture,trainingthemodel,andfine-tuningitfor\noptimalperformance.", "start_char_idx": 0, "end_char_idx": 2150, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}}, "docstore/ref_doc_info": {"ac226b84-1585-4759-add3-dc5d0af6ef65": {"node_ids": ["860863cb-ba90-4287-9ff3-de812a7cf04a"], "metadata": {"page_label": "1", "file_name": "saved_pdf.pdf", "file_path": "E:\\llama-index RAG\\data\\saved_pdf.pdf", "file_type": "application/pdf", "file_size": 64903, "creation_date": "2024-04-14", "last_modified_date": "2024-04-17"}}}}

db/db_graph_store.json

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+{"graph_dict": {}}

db/db_image__vector_store.json

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+{"embedding_dict": {}, "text_id_to_ref_doc_id": {}, "metadata_dict": {}}

db/db_index_store.json

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+{"index_store/data": {"01282bcc-a355-4256-b245-78ace39871e9": {"__type__": "vector_store", "__data__": "{\"index_id\": \"01282bcc-a355-4256-b245-78ace39871e9\", \"summary\": null, \"nodes_dict\": {\"860863cb-ba90-4287-9ff3-de812a7cf04a\": \"860863cb-ba90-4287-9ff3-de812a7cf04a\"}, \"doc_id_dict\": {}, \"embeddings_dict\": {}}"}}}